


'''simple docstring'''



from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
		import timm


if is_torch_available():
		from torch import Tensor


class 		_snake_case       (  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE						):
					lowerCAmelCase   :int				=       '''pixel_values'''
					lowerCAmelCase   :List[str]				=       False
					lowerCAmelCase   :Tuple				=       TimmBackboneConfig




					def __init__(   self				,				_lowerCamelCase				,				**_lowerCamelCase):
								requires_backends(self				,				"""timm""")
								super().__init__(__UpperCAmelCase)
								UpperCAmelCase__	:       List[str]   =  config

								if config.backbone is None:
											raise ValueError("""backbone is not set in the config. Please set it to a timm model name.""")

								if config.backbone not in timm.list_models():
											raise ValueError(f'''backbone {config.backbone} is not supported by timm.''')

								if hasattr(__UpperCAmelCase				,				"""out_features""") and config.out_features is not None:
											raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead.""")

								UpperCAmelCase__	:       str   =  getattr(__UpperCAmelCase				,				"""use_pretrained_backbone"""				,				__UpperCAmelCase)
								if pretrained is None:
											raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False.""")

								# We just take the final layer by default. This matches the default for the transformers models.
								UpperCAmelCase__	:       Optional[int]   =  config.out_indices if getattr(__UpperCAmelCase				,				"""out_indices"""				,				__UpperCAmelCase) is not None else (-1,)

								UpperCAmelCase__	:       str   =  timm.create_model(
								    config.backbone				,				pretrained=__UpperCAmelCase				,				features_only=config.features_only				,				in_chans=config.num_channels				,				out_indices=__UpperCAmelCase				,				**__UpperCAmelCase				,				)
								# These are used to control the output of the model when called. If output_hidden_states is True, then
								# return_layers is modified to include all layers.
								UpperCAmelCase__	:       str   =  self._backbone.return_layers
								UpperCAmelCase__	:       Any   =  {layer['''module''']: str(__UpperCAmelCase) for i, layer in enumerate(self._backbone.feature_info.info)}
								super()._init_backbone(__UpperCAmelCase)




					@classmethod
					def 					snake_case__    (   cls				,				_lowerCamelCase				,				*_lowerCamelCase				,				**_lowerCamelCase):
								requires_backends(cls				,				["""vision""", """timm"""])
								from ...models.timm_backbone import TimmBackboneConfig

								UpperCAmelCase__	:       Optional[Any]   =  kwargs.pop("""config"""				,				TimmBackboneConfig())

								UpperCAmelCase__	:       str   =  kwargs.pop("""use_timm_backbone"""				,				__UpperCAmelCase)
								if not use_timm:
											raise ValueError("""use_timm_backbone must be True for timm backbones""")

								UpperCAmelCase__	:       Optional[int]   =  kwargs.pop("""num_channels"""				,				config.num_channels)
								UpperCAmelCase__	:       List[str]   =  kwargs.pop("""features_only"""				,				config.features_only)
								UpperCAmelCase__	:       Optional[int]   =  kwargs.pop("""use_pretrained_backbone"""				,				config.use_pretrained_backbone)
								UpperCAmelCase__	:       Optional[int]   =  kwargs.pop("""out_indices"""				,				config.out_indices)
								UpperCAmelCase__	:       int   =  TimmBackboneConfig(
								    backbone=__UpperCAmelCase				,				num_channels=__UpperCAmelCase				,				features_only=__UpperCAmelCase				,				use_pretrained_backbone=__UpperCAmelCase				,				out_indices=__UpperCAmelCase				,				)
								return super()._from_config(__UpperCAmelCase				,				**__UpperCAmelCase)




					def 					snake_case__    (   self				,				_lowerCamelCase):
								pass




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase=None				,				_lowerCamelCase=None				,				_lowerCamelCase=None				,				**_lowerCamelCase):
								UpperCAmelCase__	:       Optional[int]   =  return_dict if return_dict is not None else self.config.use_return_dict
								UpperCAmelCase__	:       str   =  (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								UpperCAmelCase__	:       Any   =  output_attentions if output_attentions is not None else self.config.output_attentions

								if output_attentions:
											raise ValueError("""Cannot output attentions for timm backbones at the moment""")

								if output_hidden_states:
											# We modify the return layers to include all the stages of the backbone
											UpperCAmelCase__	:       Dict   =  self._all_layers
											UpperCAmelCase__	:       int   =  self._backbone(__UpperCAmelCase				,				**__UpperCAmelCase)
											UpperCAmelCase__	:       Any   =  self._return_layers
											UpperCAmelCase__	:       Union[str, Any]   =  tuple(hidden_states[i] for i in self.out_indices)
								else:
											UpperCAmelCase__	:       Optional[int]   =  self._backbone(__UpperCAmelCase				,				**__UpperCAmelCase)
											UpperCAmelCase__	:       List[Any]   =  None

								UpperCAmelCase__	:       str   =  tuple(__UpperCAmelCase)
								UpperCAmelCase__	:       int   =  tuple(__UpperCAmelCase) if hidden_states is not None else None

								if not return_dict:
											UpperCAmelCase__	:       Optional[Any]   =  (feature_maps,)
											if output_hidden_states:
														UpperCAmelCase__	:       Tuple   =  output + (hidden_states,)
											return output

								return BackboneOutput(feature_maps=__UpperCAmelCase				,				hidden_states=__UpperCAmelCase				,				attentions=__UpperCAmelCase)


import string
from math import logaa






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					List[str]      		=					document.translate(
							    str.maketrans(''''''     ,				''''''     ,				string.punctuation		)		).replace('''\n'''     ,				''''''		)
							A      :					Optional[int]      		=					document_without_punctuation.split(''' '''		)  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()]		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Dict      		=					corpus.lower().translate(
							    str.maketrans(''''''     ,				''''''     ,				string.punctuation		)		)  # strip all punctuation and replace it with ''
							A      :					Dict      		=					corpus_without_punctuation.split('''\n'''		)
							A      :					List[Any]      		=					term.lower()
							return (len([doc for doc in docs if term in doc]		), len(lowerCamelCase_		))






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_=False		):
							if smoothing:
														if n == 0:
																					raise ValueError('''log10(0) is undefined.'''		)
														return round(1 + logaa(n / (1 + df)		)     ,				3		)

							if df == 0:
														raise ZeroDivisionError('''df must be > 0'''		)
							elif n == 0:
														raise ValueError('''log10(0) is undefined.'''		)
							return round(logaa(n / df		)     ,				3		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							return round(tf * idf     ,				3		)






